{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T23:18:47Z","timestamp":1792192727646,"version":"4.4.0"},"reference-count":95,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T00:00:00Z","timestamp":1738540800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100008982","name":"Qatar University","doi-asserted-by":"publisher","award":["UREP30-196-2-058"],"award-info":[{"award-number":["UREP30-196-2-058"]}],"id":[{"id":"10.13039\/100008982","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Upper limb disabilities, often caused by conditions such as stroke or neurological disorders, severely limit an individual\u2019s ability to perform essential daily tasks, leading to a significant reduction in quality of life. The development of effective rehabilitation technologies is crucial to restoring motor function and improving patient outcomes. This systematic review examines the application of machine learning and deep learning techniques in myoelectric-controlled systems for upper limb rehabilitation, focusing on the use of electroencephalography and electromyography signals. By integrating non-invasive signal acquisition methods with advanced computational models, the review highlights how these technologies can enhance the accuracy and efficiency of rehabilitation devices. A comprehensive search of literature published between January 2015 and July 2024 led to the selection of fourteen studies that met the inclusion criteria. These studies showcase various approaches in decoding motor intentions and controlling assistive devices, with models such as Long Short-Term Memory Networks, Support Vector Machines, and Convolutional Neural Networks showing notable improvements in control precision. However, challenges remain in terms of model robustness, computational complexity, and real-time applicability. This systematic review aims to provide researchers with a deeper understanding of the current advancements and challenges in this field, guiding future research efforts to overcome these barriers and facilitate the transition of these technologies from experimental settings to practical, real-world applications.<\/jats:p>","DOI":"10.3390\/bioengineering12020144","type":"journal-article","created":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T04:36:51Z","timestamp":1738557411000},"page":"144","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Machine Learning- and Deep Learning-Based Myoelectric Control System for Upper Limb Rehabilitation Utilizing EEG and EMG Signals: A Systematic Review"],"prefix":"10.3390","volume":"12","author":[{"given":"Tala","family":"Zaim","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Qatar University, Doha 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sara","family":"Abdel-Hadi","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Qatar University, Doha 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rana","family":"Mahmoud","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Qatar University, Doha 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7068-9112","authenticated-orcid":false,"given":"Amith","family":"Khandakar","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Qatar University, Doha 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7657-679X","authenticated-orcid":false,"given":"Seyed Mehdi","family":"Rakhtala","sequence":"additional","affiliation":[{"name":"School of Engineering, University of the West of England, Bristol BS16 1QY, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0744-8206","authenticated-orcid":false,"given":"Muhammad E. H.","family":"Chowdhury","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Qatar University, Doha 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mang, C.S., and Peters, S. (2021). Advancing motor rehabilitation for adults with chronic neurological conditions through increased involvement of kinesiologists: A perspective review. BMC Sports Sci. Med. Rehabil., 13.","DOI":"10.1186\/s13102-021-00361-6"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Badawi, Y., and Nishimune, H. (2020). Impairment Mechanisms and Intervention Approaches for Aged Human Neuromuscular Junctions. Front. Mol. Neurosci., 13.","DOI":"10.3389\/fnmol.2020.568426"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1007\/s42235-022-00289-8","article-title":"Systematic Review on Wearable Lower Extremity Robotic Exoskeletons for Assisted Locomotion","volume":"20","author":"Qiu","year":"2022","journal-title":"J. Bionic Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s12984-021-00815-5","article-title":"Systematic review on wearable lower-limb exoskeletons for gait training in neuromuscular impairments","volume":"18","year":"2021","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"V\u00e9lez-Guerrero, M.A., Callejas-Cuervo, M., and Mazzoleni, S. (2021). Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review. Sensors, 21.","DOI":"10.3390\/s21062146"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1186\/s12984-017-0284-4","article-title":"Improved prosthetic hand control with concurrent use of myoelectric and inertial measurements","volume":"14","author":"Krasoulis","year":"2017","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_7","first-page":"16","article-title":"Experimental Evaluation of the Impact of SEMG Interfaces in Enhancing Embodiment of Virtual Myoelectric Prostheses","volume":"21","author":"Connan","year":"2024","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gaudry, K.S., Ayaz, H., Bedows, A., Celnik, P., Eagleman, D., Grover, P., Illes, J., Rao, R.P.N., Robinson, J.T., and Thyagarajan, K. (2021). Projections and the Potential Societal Impact of the Future of Neurotechnologies. Front. Neurosci., 15.","DOI":"10.3389\/fnins.2021.658930"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Waldert, S. (2016). Invasive vs. Non-Invasive Neuronal Signals for Brain-Machine Interfaces: Will One Prevail?. Front. Neurosci., 10.","DOI":"10.3389\/fnins.2016.00295"},{"key":"ref_10","unstructured":"Firuzi, R., Ahmadyani, H., Abdi, M.F., Naderi, D., Hassan, J., and Bokani, A. (2022). Decoding Neural Signals with Computational Models: A Systematic Review. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s42979-023-02160-x","article-title":"Review of Neural Interfaces: Means for Establishing Brain\u2013Machine Communication","volume":"4","author":"Khuntia","year":"2023","journal-title":"SN Comput. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"12","DOI":"10.7202\/1044618ar","article-title":"\u201cInvasive\u201d and \u201cNon-Invasive\u201d Technologies in Neuroscience Communication","volume":"6","author":"Gaillard","year":"2018","journal-title":"Bio\u00e9thiqueOnline"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s13054-023-04779-x","article-title":"Analysis and Applications of Respiratory Surface EMG: Report of a Round Table Meeting","volume":"28","author":"Jonkman","year":"2024","journal-title":"Crit. Care"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Medved, V., Medved, S., and Kova\u010d, I. (2020). Critical Appraisal of Surface Electromyography (sEMG) as a Taught Subject and Clinical Tool. Front. Neurol., 11.","DOI":"10.3389\/fneur.2020.560363"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Campanini, I., Disselhorst-Klug, C., Rymer, W.Z., and Merletti, R. (2020). Surface EMG in Clinical Assessment and Neurorehabilitation: Barriers Limiting Its Use. Front. Neurol., 11.","DOI":"10.3389\/fneur.2020.00934"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1007\/s13534-023-00281-z","article-title":"Recent trends and challenges of surface electromyography in prosthetic control","volume":"13","author":"Yadav","year":"2023","journal-title":"Biomed. Eng. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Suo, M., Zhou, L., Wang, J., Huang, H., Zhang, J., Sun, T., Liu, X., Chen, X., Song, C., and Li, Z. (2024). The Application of Surface Electromyography Technology in Evaluating Paraspinal Muscle Function. Diagnostics, 14.","DOI":"10.3390\/diagnostics14111086"},{"key":"ref_18","first-page":"67","article-title":"The Applied Principles of EEG Analysis Methods in Neuroscience and Clinical Neurology","volume":"10","author":"Zhang","year":"2023","journal-title":"Mil. Med. Res."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Sabio, J., Williams, N.S., McArthur, G.M., and Badcock, N.A. (2024). A scoping review on the use of consumer-grade EEG devices for research. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0291186"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sun, C., and Mou, C. (2023). Survey on the Research Direction of EEG-Based Signal Processing. Front. Neurosci., 17.","DOI":"10.3389\/fnins.2023.1203059"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/s42979-024-02773-w","article-title":"Emerging Trends in EEG Signal Processing: A Systematic Review","volume":"5","author":"Sharma","year":"2024","journal-title":"SN Comput. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1186\/s12984-022-01032-4","article-title":"Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: A systematic review","volume":"19","author":"Campagnini","year":"2022","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Fu, J., Choudhury, R., Hosseini, S.M., Simpson, R., and Park, J.-H. (2022). Myoelectric Control Systems for Upper Limb Wearable Robotic Exoskeletons and Exosuits\u2014A Systematic Review. Sensors, 22.","DOI":"10.3390\/s22218134"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.dcan.2015.02.004","article-title":"Bio-signal based control in assistive robots: A survey","volume":"1","author":"Hu","year":"2015","journal-title":"Digit. Commun. Netw."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.bspc.2019.02.011","article-title":"A review on EMG-based motor intention prediction of continuous human upper limb motion for human-robot collaboration","volume":"51","author":"Feleke","year":"2019","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lotti, N., Xiloyannis, M., Missiroli, F., Chiaradia, D., Frisoli, A., Sanguineti, V., and Masia, L. (2020, January 15). Intention-detection strategies for upper limb exosuits: Model-based myoelectric vs dynamic-based control. Proceedings of the 2020 8th IEEE RAS\/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), New York, NY, USA.","DOI":"10.1109\/BioRob49111.2020.9224284"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"7420","DOI":"10.1016\/j.eswa.2012.01.102","article-title":"Feature reduction and selection for EMG signal classification","volume":"39","author":"Phinyomark","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1007\/978-3-642-39068-5_25","article-title":"EMG-Based Neural Network Control of an Upper-Limb Power-Assist Exoskeleton Robot","volume":"Volume 7952","author":"Hutchison","year":"2013","journal-title":"Advances in Neural Networks\u2014ISNN 2013"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Jana, M., Barua, B.G., and Hazarika, S.M. (2019, January 23\u201326). Design and Development of a Finger Exoskeleton for Motor Rehabilitation using Elec tromyography Signals. Proceedings of the 2019 23rd International Conference on Mechatronics Technology (ICMT), Salerno, Italy.","DOI":"10.1109\/ICMECT.2019.8932126"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Tang, Z., Zhang, K., Sun, S., Gao, Z., Zhang, L., and Yang, Z. (2014). An Upper-Limb Power-Assist Exoskeleton Using Proportional Myoelectric Control. Sensors, 14.","DOI":"10.3390\/s140406677"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1109\/TOH.2015.2417570","article-title":"AnEMG-controlled robotic hand exoskeleton for bilateral rehabilitation","volume":"8","author":"Leonardis","year":"2015","journal-title":"IEEE Trans. Haptics"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.patrec.2017.04.007","article-title":"Learning assistive strategies for exoskeleton robots from user-robot physical interaction","volume":"99","author":"Hamaya","year":"2017","journal-title":"Pattern Recognit. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1007\/s11370-020-00328-5","article-title":"Comprehensive review on brain-controlled mobile robots and robotic arms based on electroencephalography signals","volume":"13","author":"Aljalal","year":"2020","journal-title":"Intell. Serv. Robot."},{"key":"ref_34","unstructured":"Tyromotion, M. (2024, December 22). Interactive Sensor-Based Rehabilitation Device. Available online: https:\/\/tyromotion.com\/en\/products\/myro\/."},{"key":"ref_35","unstructured":"Myomo Inc. (2024, December 22). What Is a MyoPro Orthosis?. Available online: https:\/\/myomo.com\/what-is-a-myopro-orthosis\/."},{"key":"ref_36","unstructured":"Neurolutions Inc. (2024, December 22). Home. Available online: https:\/\/www.neurolutions.com\/."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Brambilla, C., Pirovano, I., Mira, R.M., Rizzo, G., Scano, A., and Mastropietro, A. (2021). Combined Use of EMG and EEG Techniques for Neuromotor Assessment in Rehabilitative Applications: A Systematic Review. Sensors, 21.","DOI":"10.3390\/s21217014"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"ii32","DOI":"10.1136\/jnnp.2005.069211","article-title":"The Basics of Electromyography","volume":"76","author":"Mills","year":"2005","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1186\/s12984-021-00892-6","article-title":"EEG hyperscanning in motor rehabilitation: A position paper","volume":"18","author":"Short","year":"2021","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","article-title":"Machine Learning: Algorithms, Real-World Applications and Research Directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1007\/s42979-021-00815-1","article-title":"Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"333","DOI":"10.23736\/S1973-9087.22.07509-8","article-title":"Rehabilitation Definition for Research Purposes. A Global Stakeholders\u2019 Initiative by Cochrane Rehabilitation","volume":"58","author":"Negrini","year":"2022","journal-title":"Eur. J. Phys. Rehabil. Med."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1361","DOI":"10.1007\/s12551-020-00770-w","article-title":"Review on Electromyography Signal Acquisition and Processing","volume":"12","author":"Gohel","year":"2020","journal-title":"Biophys. Rev."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1007\/s40430-021-02919-4","article-title":"Upper Limbs\u2019 Assistive Devices for Stroke Rehabilitation: A Systematic Review on Design Engineering Solutions","volume":"43","author":"Barbosa","year":"2021","journal-title":"J. Braz. Soc. Mech. Sci. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Al-Ayyad, M., Owida, H.A., De Fazio, R., Al-Naami, B., and Visconti, P. (2023). Electromyography Monitoring Systems in Rehabilitation: A Review of Clinical Applications, Wearable Devices and Signal Acquisition Methodologies. Electronics, 12.","DOI":"10.3390\/electronics12071520"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"16795","DOI":"10.1007\/s12652-023-04688-5","article-title":"A Motion Recognition Model for Upper-Limb Rehabilitation Exercises","volume":"14","author":"Li","year":"2023","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_47","first-page":"98","article-title":"Machine Learning in Robot-Assisted Upper Limb Rehabilitation: A Focused Review","volume":"18","author":"Ai","year":"2021","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Khoramdel, J., Moori, A., Moghaddam, M.M., and Najafi, E. (2021, January 17). Compensatory Movement Detection in Upper Limb Rehabilitation with Deep Learning Methods. Proceedings of the 2021 9th RSI International Conference on Robotics and Mechatronics (ICRoM), Tehran, Iran.","DOI":"10.1109\/ICRoM54204.2021.9663458"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Rainio, O., Teuho, J., and Kl\u00e9n, R. (2024). Evaluation Metrics and Statistical Tests for Machine Learning. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-56706-x"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1819","DOI":"10.1109\/TKDE.2013.39","article-title":"A Review on Multi-Label Learning Algorithms","volume":"26","author":"Zhang","year":"2014","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"101660","DOI":"10.1016\/j.rineng.2023.101660","article-title":"Using machine learning algorithms for grasp strength recognition in rehabilitation planning","volume":"21","author":"Boka","year":"2024","journal-title":"Results Eng."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Li, X., Lu, Q., Chen, P., Gong, S., Yu, X., He, H., and Li, K. (2023). Assistance level quantification-based human-robot interaction space reshaping for rehabilitation training. Front. Neurorobot., 17.","DOI":"10.3389\/fnbot.2023.1161007"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1002\/aisy.202300094","article-title":"ConvNets for Electroencephalographic Decoding of Attempted Arm and Hand Movements of People with Spinal Cord Injury","volume":"5","author":"Cancino","year":"2023","journal-title":"Adv. Intell. Syst."},{"key":"ref_54","first-page":"2399","article-title":"Comprehensive Analysis of an EMG Based Human-Robot Interface Using Various Machine Learning Techniques","volume":"152","author":"Prakash","year":"2023","journal-title":"Philipp. J. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.neucom.2023.01.061","article-title":"Hierarchical approach for fusion of electroencephalography and electromyography for predicting finger movements and kinematics using deep learning","volume":"527","author":"Das","year":"2023","journal-title":"Neurocomputing"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Hagengruber, A., Leipscher, U., Eskofier, B.M., and Vogel, J. (2022). A New Labeling Approach for Proportional Electromyographic Control. Sensors, 22.","DOI":"10.3390\/s22041368"},{"key":"ref_57","first-page":"935","article-title":"Biofeedback Upper Limb Assessment Using Electroencephalogram, Electromyographic and Electrocardiographic with Machine Learning in Signal Classification","volume":"30","author":"Lew","year":"2022","journal-title":"Eng. Lett."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"46834","DOI":"10.1109\/ACCESS.2022.3170906","article-title":"Brain-Computer Interface Controlled Functional Electrical Stimulation: Evaluation with Healthy Subjects and Spinal Cord Injury Patients","volume":"10","author":"Antelis","year":"2022","journal-title":"IEEE Access"},{"key":"ref_59","first-page":"1171","article-title":"Design of Human Adaptive Mechatronics Controller for Upper Limb Motion Intention Prediction","volume":"71","author":"Hwang","year":"2022","journal-title":"Comput. Mater. Contin."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Idowu, O.P., Ilesanmi, A.E., Li, X., Samuel, O.W., Fang, P., and Li, G. (2021). An Integrated Deep Learning Model for Motor Intention Recognition of Multi-Class EEG Signals in Upper Limb Amputees. Comput. Methods Programs Biomed., 206.","DOI":"10.1016\/j.cmpb.2021.106121"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Silva-Acosta, V.d.C., Rom\u00e1n-God\u00ednez, I., Torres-Ramos, S., and Salido-Ruiz, R.A. (2021). Automatic Estimation of Continuous Elbow Flexion\u2013Extension Movement Based on Electromyographic and Electroencephalographic Signals. Biomed. Signal Process. Control, 70.","DOI":"10.1016\/j.bspc.2021.102950"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1186\/s12984-019-0512-1","article-title":"Detection of Movement Onset Using EMG Signals for Upper-Limb Exoskeletons in Reaching Tasks","volume":"16","author":"Trigili","year":"2019","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"52889","DOI":"10.1109\/ACCESS.2018.2869687","article-title":"Decoding Asynchronous Reaching in Electroencephalography Using Stacked Autoencoders","volume":"6","author":"Pei","year":"2018","journal-title":"IEEE Access"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Kim, Y.J., Park, S.W., Yeom, H.G., Bang, M.S., Kim, J.S., Chung, C.K., and Kim, S. (2015). A study on a robot arm driven by three-dimensional trajectories predicted from non-invasive neural signals. Biomed. Eng. Online, 14.","DOI":"10.1186\/s12938-015-0075-8"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"S85","DOI":"10.1111\/j.1526-4637.2007.00372.x","article-title":"Real-time data collection for pain: Appraisal and current status","volume":"8","author":"Stone","year":"2007","journal-title":"Pain Med."},{"key":"ref_66","first-page":"1171","article-title":"Retrospective studies and chart reviews","volume":"49","author":"Hess","year":"2004","journal-title":"Respir. Care"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Kok, C.L., Ho, C.K., Tan, F.K., and Koh, Y.Y. (2024). Machine Learning-Based Feature Extraction and Classification of EMG Signals for Intuitive Prosthetic Control. Appl. Sci., 14.","DOI":"10.3390\/app14135784"},{"key":"ref_68","first-page":"7","article-title":"Selecting critical features for data classification based on machine learning methods","volume":"52","author":"Chen","year":"2020","journal-title":"J. Big Data"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.1007\/s10115-023-02010-5","article-title":"Feature selection techniques for machine learning: A survey","volume":"66","author":"Theng","year":"2023","journal-title":"Knowl. Inf. Syst."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Wu, M., Zhou, J., Peng, Y., Wang, S., and Zhang, Y. (2024). Deep Learning for Image Classification: A Review. Lecture Notes in Electrical Engineering, Springer.","DOI":"10.1007\/978-981-97-1335-6_31"},{"key":"ref_71","unstructured":"Basmajian, J.V., and De Luca, C.J. (1985). Muscles Alive: Their Functions Revealed by Electromyography, Williams & Wilkins. [5th ed.]."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1001\/jama.2009.116","article-title":"Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms","volume":"301","author":"Kuiken","year":"2009","journal-title":"JAMA"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/1743-0003-7-21","article-title":"Study of stability of time-domain features for electromyographic pattern recognition","volume":"7","author":"Tkach","year":"2010","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_74","unstructured":"Aceves-Fern\u00e1ndez, M.A. (2019). A Review of EMG Techniques for Detection of Gait Disorders. Artificial Intelligence-Applications in Medicine and Biology, IntechOpen."},{"key":"ref_75","unstructured":"Moctar, S.M.S., Rida, I., and Boudaoud, S. (2023). Time-Domain Features for sEMG Signal Classification: A Brief Survey, JETSAN."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1162\/tacl_a_00675","article-title":"A Closer Look at Classification Evaluation Metrics and a Critical Reflection of Common Evaluation Practice","volume":"12","author":"Opitz","year":"2024","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Arteaga, M.V., Castiblanco, J.C., Mondragon, I.F., Colorado, J.D., and Alvarado-Rojas, C. (December, January 29). EMG-based adaptive trajectory generation for an exoskeleton model during hand rehabilitation exercises. Proceedings of the 2020 8th IEEE RAS\/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), New York, NY, USA.","DOI":"10.1109\/BioRob49111.2020.9224328"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"585","DOI":"10.20965\/jrm.2012.p0585","article-title":"Study on the sEMG Driven Upper Limb Exoskeleton Rehabilitation Device in Bilateral Rehabilitation","volume":"24","author":"Pang","year":"2012","journal-title":"J. Robot. Mechatron."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Loconsole, C., Leonardis, D., Barsotti, M., Solazzi, M., Frisoli, A., Bergamasco, M., Troncossi, M., Foumashi, M.M., Mazzotti, C., and Castelli, V.P. (2013, January 14\u201317). An emg-based robotic hand exoskeleton for bilateral training of grasp. Proceedings of the 2013 World Haptics Conference (WHC), Daejeon, Republic of Korea.","DOI":"10.1109\/WHC.2013.6548465"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Ngeo, J., Tamei, T., Shibata, T., Orlando, M.F., Behera, L., Saxena, A., and Dutta, A. (2013, January 3\u20137). Control of an optimal finger exoskeleton based on continuous joint angle estimation from EMG signals. Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan.","DOI":"10.1109\/EMBC.2013.6609506"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Zeng, H., Li, K., Wei, N., Song, R., and Tian, X.A. (2018, January 25\u201327). Semg-controlled robotic hand exoskeleton for rehabilitation in post-stroke individuals. Proceedings of the 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS), Shenzhen, China.","DOI":"10.1109\/CBS.2018.8612211"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1109\/TBCAS.2019.2950145","article-title":"Myoelectric control of a soft hand exoskeleton using kinematic synergies","volume":"13","author":"Burns","year":"2019","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"137809","DOI":"10.1109\/ACCESS.2021.3118281","article-title":"RobHand: A Hand Exoskeleton with Real-Time EMG-Driven Embedded Control. Quantifying Hand Gesture Recognition Delays for Bilateral Rehabilitation","volume":"9","author":"Cisnal","year":"2021","journal-title":"IEEE Access"},{"key":"ref_84","unstructured":"Chae, Y., Jo, S., and Jeong, J. (May, January 27). Brain-actuated humanoid robot navigation control using asynchronous brain-computer interface. Proceedings of the 5th International IEEE\/EMBS Conference on Neural Engineering, Cancun, Mexico."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Chae, Y., Jeong, J., and Jo, S. (2011, January 25\u201330). Noninvasive brain-computer interface-based control of humanoid navigation. Proceedings of the 2011 IEEE\/RSJ International Conference on Intelligent Robots and Systems, San Francisco, CA, USA.","DOI":"10.1109\/IROS.2011.6048044"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A tutorial on support vector machines for pattern recognition","volume":"2","author":"Burges","year":"1998","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.robot.2015.01.007","article-title":"Interval type-2 fuzzy logic based multiclass ANFIS algorithm for real-time EEG based movement control of a robot arm","volume":"68","author":"Bhattacharyya","year":"2015","journal-title":"Robot. Auton. Syst."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Choi, K., and Cichocki, A. (2008, January 2\u20135). Control of a wheelchair by motor imagery in real time. Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning, Daejeon, Republic of Korea.","DOI":"10.1007\/978-3-540-88906-9_42"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1007\/s00421-011-2029-6","article-title":"Control of a vehicle with EEG signals in real-time and system evaluation","volume":"112","author":"Choi","year":"2011","journal-title":"Eur. J. Appl. Physiol."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1007\/s40846-018-0431-9","article-title":"Robot navigation using a brain computer interface based on motor imagery","volume":"39","author":"Aljalal","year":"2018","journal-title":"J. Med. Biol. Eng."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Song, W., Wang, X., Zheng, S., and Lin, Y. (2014, January 26\u201327). Mobile robot control by BCI based on motor imagery. Proceedings of the 2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Hangzhou, China.","DOI":"10.1109\/IHMSC.2014.194"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"11804","DOI":"10.1007\/s10489-024-05747-w","article-title":"A comprehensive review of model compression techniques in machine learning","volume":"54","author":"Dantas","year":"2024","journal-title":"Appl. Intell."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.jneumeth.2003.10.009","article-title":"EEGLAB: An open-source toolbox for analysis of single-trial EEG dynamics including independent component analysis","volume":"134","author":"Delorme","year":"2004","journal-title":"J. Neurosci. Methods"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A., and Hinton, G. (2013, January 26\u201331). Speech recognition with deep recurrent neural networks. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Omar, A., Abbas, M., Yousef, K., and Khalil, K. (2023, January 18\u201320). An Efficient Deep Learning Approach for sEMG Hand Gesture Classification. Proceedings of the 2023 11th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC), Alexandria, Egypt.","DOI":"10.1109\/JAC-ECC61002.2023.10479647"}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/12\/2\/144\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:26:10Z","timestamp":1760027170000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/12\/2\/144"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,3]]},"references-count":95,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["bioengineering12020144"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering12020144","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,3]]}}}